A U.K. study led by researchers at University College London (UCL) shows that a blood test performed during the initial infection period may predict who is most likely to develop long COVID.
In the study, published on Wednesday in Lancet eBioMedicine, researchers analyzed the plasma proteomes from 54 healthcare workers with confirmed SARS-CoV-2 infection and 102 without infection (for a total study group of 156 people) over a six-week period. Proteomes are usually stable; changes in protein levels reflect the disruption of normal biological processes.
The researchers preselected 91 proteins for analysis. The proteins were selected with the intent of going beyond the conventional inflammatory biomarkers to present a more COVID-specific profile to analyze; as there is substantial evidence pointing to SARS-CoV-2’s role in disrupting cognitive and neurological functions, neuroinflammatory biomarkers made up about 25% of the proteins selected.
The researchers found changes in the levels of 12 of the proteins in infected workers that persisted over the six-week period, showing that the virus disrupted normal processes in the body. Furthermore, the degree of changes tracked with the severity of symptoms.
In addition, the proteome profiles for the SARS-CoV-2-positive among the study group looked different not only from those of the controls, but also from those of people who had been hospitalized due to more-severe COVID symptoms. The profiles of those hospitalized reflected proteomic disturbances due to stressors such as immobilization and mechanical ventilation, not just from the infection itself.
“This can overshadow what may be the subtle changes occurring from infection,” the researchers noted in the study.
Using an artificial intelligence (AI) algorithm, the researchers were able to identify a proteome signature in the various protein levels and create a proteomic profile that successfully predicted whether or not a SARS-CoV-2-positive person would go on to report the lingering symptoms that signify long COVID a year after infection. The results showed that abnormal levels of 20 of the proteins at the time of first infection were predictive of symptoms that had persisted a year later. Most of these signature proteins were linked to anticoagulant and anti-inflammatory processes.
The algorithm was able to distinguish all of the 11 infected healthcare workers who had reported at least one persistent symptom at one year from infected healthcare workers who had not reported persistent symptoms at one year.
A separate AI tool estimated a possible error rate of 6% for the method.
The researchers noted that the major limitation in their study was that it was a single-center study with a relatively small sample size. However, if these results are borne out through larger multicenter studies, the findings could lead to the use of a cost-effective and efficient test that could predict people’s likelihood of developing long COVID.
“The method of analysis we used is readily available in hospitals and is high-throughput, meaning it can analyze thousands of samples in an afternoon,” said lead author Dr. Gaby Captur, of the MRC Unit for Lifelong Health and Ageing at UCL.
Senior author Dr. Wendy Heywood, of UCL Great Ormond Street Institute of Child Health and Great Ormond Street Hospital, added: “If we can identify people who are likely to develop long COVID, this opens the door to trialing treatments such as antivirals at this earlier, initial infection stage, to see if it can reduce the risk of later long COVID.”